Conference Paper

Grid-based large-scale Web3D collaborative virtual environment

DOI: 10.1145/1229390.1229412 Conference: Proceeding of the Twelfth International Conference on 3D Web Technology, Web3D 2007, Perugia, Italy, April 15-18, 2007
Source: DBLP


This paper presents a grid-based large-scale web3D collaborative virtual environment that has the capability of scaling across multiple geographically dispersed resources. The architecture consists of distributed mobile agents working cooperatively in supporting and managing the web3D collaborative virtual environments. The mobile agents' tasks include managing persistency and consistency of the virtual worlds, maintaining reliability and efficiency of user interactions, ensuring security and integrity of data and systems. The mobile agents are autonomous and have the ability of migrating among hosts to maximize resource utilizations. Grid technologies allow the mobile agents to execute and communicate securely in multiple administrative domains. Grid-based scheduling components and polices are integrated to provide intelligent resource optimizations. Furthermore, a better load-balancing can be achieved by utilizing additional or more accurate information like data-user proximity and hosts' workload. The result will be a more scalable and robust architecture for supporting large-scale web3D collaborative virtual environment.

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    ABSTRACT: Enterprise distributed real-time and embedded (DRE) publish/subscribe (pub/sub) systems manage resources and data that are vital to users. Cloud com- puting—where computing resources are provisioned elastically and leased as a service—is an increasingly popular deployment paradigm. Enterprise DRE pub/- sub systems can leverage cloud computing provisioning services to execute needed functionality when on-site computing resources are not available. Although cloud computing provides flexible on-demand computing and networking resources, enterprise DRE pub/sub systems often cannot accurately characterize their be- havior a priori for the variety of resource configurations cloud computing sup- plies (e.g., CPU and network bandwidth), which makes it hard for DRE systems to leverage conventional cloud computing platforms. This paper provides two contributions to the study of how autonomic configu- ration of DRE pub/sub middleware can provision and use on-demand cloud re- sources effectively. We first describe how supervised machine learning can con- figure DRE pub/sub middleware services and transport protocols autonomically to support end-to-end quality-of-service (QoS) requirements based on cloud com- puting resources. We then present results that empirically validate how comput- ing and networking resources affect enterprise DRE pub/sub system QoS. These results show how supervised machine learning can configure DRE pub/sub mid- dleware adaptively in < 10 μsec with bounded time complexity to support key QoS reliability and latency requirements.
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